[1]JIANG Feng,LI Bo,YAO Hong-xun,et al.Manifold learning and manifold alignmentbased on coupled linear projections[J].CAAI Transactions on Intelligent Systems,2010,5(6):476-481.
Copy

Manifold learning and manifold alignmentbased on coupled linear projections

References:
[1]TENENBAUM J, SILVA V, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 5500(290): 23192323. 
[2]ROWEIS S, SAUL L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 5500(290): 23232326.
[3]SAUL L, ROWEIS S. Think globally, fit locally: unsupervised learning of low dimensional manifolds[J]. The Journal of Machine Learning Research, 2003, 4: 119155.
[4]BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 13731396.
[5]YAN S, XU D, ZHANG B, ZHANG H, YANG Q. Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 4051.
[6]BENGIO Y, PAIEMENT J, VINCENT P, et al. Outofsample extensions for LIE, Isomap, MDS, eigenmaps, and spectral clustering[C]//Advances in Neural Information Processing Systems.Cambrige: MIT Press, 2004: 177184.
[7]HAM J, LEE D, SAUL L. Semisupervised alignment of manifolds[C]//Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence. Edinburgh, UK, 2005: 120127.
[8]CONG Haifeng, PAN Chunhong, YANG Qing. A semisupervised framework for mapping data to the intrinsic manifold[C]//Tenth IEEE International Conference on Computer Vision 2005. [S.l.], 2005: 98105.
[9]XIONG Liang, WANG Fei, ZHANG Changshui. Semidefinite manifold alignment[J]. Lecture Notes in Computer Science, 2007, 4701: 773.
[10]RASMUSSEN C, WILLIAMS C. Gaussian processes for machine learning[M]. Cambrige: The MIT Press, 2006: 101107. 
[11]WANG C, MAHADEVAN S. Manifold alignment using procrustes analysis[C]//Proceedings of the 25th International Conference on Machine Learning. Helsinki, Finland, 2008: 11201127.
[12]NENE S, NAYAR S, MURASE H. Columbia object image library (coil20)[R]. CUCS00696, Department of Computer Science, Columbia University.
[13]GEORGHIADES A, BELHUMEUR P, KRIEGMAN D. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643660.
Similar References:

Memo

-

Last Update: 2011-03-03

Copyright © CAAI Transactions on Intelligent Systems